Loading Our Data

Section 6.3 Loading Our Data

This chapter uses the infection_treatment dataset from the companion R reproresearchR package ([D.1.24]). Refer back to Subsection 2.3.2 to find out more about the reproresearchR package if needed.

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Instead of using the summary() or str() functions, we are going to experiment with the skim() function from the skimr package ([D.1.25]) to investigate our data.

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library(tidyverse)
library(reproresearchR)

infection <- reproresearchR::infection_treatments

library(skimr)

skim(infection)

── Data Summary ────────────────────────
                           Values   
Name                       infection
Number of rows             150      
Number of columns          2        
_______________________             
Column type frequency:              
  character                2        
________________________            
Group variables            None     

── Variable type: character ────────────────────────────────────────────────────
  skim_variable n_missing complete_rate min max empty n_unique whitespace
1 Infection             0             1   2   3     0        2          0
2 Treatment             0             1   7  13     0        3          0

We can see our data nice and loaded. There are two columns, Infection and Treatment which are both categorical, and there are 150 rows. Thankfully, we do not have any missing data. Let’s dig a little deeper into our data.

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